Analisis inteligente de datos: Trabajo practico
Adrian Norberto Marino
2021/08/01
options(warn=-1)
library(pacman)
p_load(dplyr)
source('../lib/import.R')
#
# Es una librería de funciones comunes desarrolladas a partir de este TP.
import('../lib/common-lib.R')## [1] "-> './reflection.R' script loadded successfuly!"
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## ---
## biotools version 4.1
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# Funciones especificas para este TP.
import('../scripts/helper_functions.R')## [1] "-> '../lib/pca.R' script loadded successfuly!"
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## [1] "-> '../scripts/helper_functions.R' script loadded successfuly!"set.seed(1)1. Cargamos el dataset
1.1. Cargamos el dataset nasa-asteroids y excluirnos observaciones con valores faltaste.
dataset <- loadcsv('../datasets/nasa.csv')
str(dataset)## 'data.frame': 4687 obs. of 40 variables:
## $ Neo.Reference.ID : int 3703080 3723955 2446862 3092506 3514799 3671135 2495323 2153315 2162463 2306383 ...
## $ Name : int 3703080 3723955 2446862 3092506 3514799 3671135 2495323 2153315 2162463 2306383 ...
## $ Absolute.Magnitude : num 21.6 21.3 20.3 27.4 21.6 19.6 19.6 19.2 17.8 21.5 ...
## $ Est.Dia.in.KM.min. : num 0.1272 0.1461 0.2315 0.0088 0.1272 ...
## $ Est.Dia.in.KM.max. : num 0.2845 0.3266 0.5177 0.0197 0.2845 ...
## $ Est.Dia.in.M.min. : num 127.2 146.1 231.5 8.8 127.2 ...
## $ Est.Dia.in.M.max. : num 284.5 326.6 517.7 19.7 284.5 ...
## $ Est.Dia.in.Miles.min. : num 0.07905 0.09076 0.14385 0.00547 0.07905 ...
## $ Est.Dia.in.Miles.max. : num 0.1768 0.203 0.3217 0.0122 0.1768 ...
## $ Est.Dia.in.Feet.min. : num 417.4 479.2 759.5 28.9 417.4 ...
## $ Est.Dia.in.Feet.max. : num 933.3 1071.6 1698.3 64.6 933.3 ...
## $ Close.Approach.Date : chr "1995-01-01" "1995-01-01" "1995-01-08" "1995-01-15" ...
## $ Epoch.Date.Close.Approach : num 7.89e+11 7.89e+11 7.90e+11 7.90e+11 7.90e+11 ...
## $ Relative.Velocity.km.per.sec: num 6.12 18.11 7.59 11.17 9.84 ...
## $ Relative.Velocity.km.per.hr : num 22017 65210 27327 40226 35427 ...
## $ Miles.per.hour : num 13681 40519 16980 24995 22013 ...
## $ Miss.Dist..Astronomical. : num 0.419 0.383 0.051 0.285 0.408 ...
## $ Miss.Dist..lunar. : num 163.2 149 19.8 111 158.6 ...
## $ Miss.Dist..kilometers. : num 62753692 57298148 7622912 42683616 61010824 ...
## $ Miss.Dist..miles. : num 38993336 35603420 4736658 26522368 37910368 ...
## $ Orbiting.Body : chr "Earth" "Earth" "Earth" "Earth" ...
## $ Orbit.ID : int 17 21 22 7 25 40 43 22 100 30 ...
## $ Orbit.Determination.Date : chr "2017-04-06 08:36:37" "2017-04-06 08:32:49" "2017-04-06 09:20:19" "2017-04-06 09:15:49" ...
## $ Orbit.Uncertainity : int 5 3 0 6 1 1 1 0 0 0 ...
## $ Minimum.Orbit.Intersection : num 0.02528 0.18693 0.04306 0.00551 0.0348 ...
## $ Jupiter.Tisserand.Invariant : num 4.63 5.46 4.56 5.09 5.15 ...
## $ Epoch.Osculation : num 2458000 2458000 2458000 2458000 2458000 ...
## $ Eccentricity : num 0.426 0.352 0.348 0.217 0.21 ...
## $ Semi.Major.Axis : num 1.41 1.11 1.46 1.26 1.23 ...
## $ Inclination : num 6.03 28.41 4.24 7.91 16.79 ...
## $ Asc.Node.Longitude : num 314.4 136.7 259.5 57.2 84.6 ...
## $ Orbital.Period : num 610 426 644 514 496 ...
## $ Perihelion.Distance : num 0.808 0.718 0.951 0.984 0.968 ...
## $ Perihelion.Arg : num 57.3 313.1 248.4 18.7 158.3 ...
## $ Aphelion.Dist : num 2.01 1.5 1.97 1.53 1.48 ...
## $ Perihelion.Time : num 2458162 2457795 2458120 2457902 2457814 ...
## $ Mean.Anomaly : num 264.8 173.7 292.9 68.7 135.1 ...
## $ Mean.Motion : num 0.591 0.845 0.559 0.7 0.726 ...
## $ Equinox : chr "J2000" "J2000" "J2000" "J2000" ...
## $ Hazardous : chr "True" "False" "True" "False" ...1.2. Las siguientes columnas las excluimos ya sea por que son no numerica, o colineales con las columna que si son parte de nuestro analisis.
excluded_columns <- c(
'Neo.Reference.ID',
'Name',
'Close.Approach.Date',
'Epoch.Date.Close.Approach',
'Orbit.ID',
'Orbit.Determination.Date',
'Orbiting.Body',
'Est.Dia.in.Feet.min.',
'Est.Dia.in.Feet.max.',
'Est.Dia.in.M.min.',
'Est.Dia.in.M.max.',
'Est.Dia.in.KM.min.',
'Est.Dia.in.KM.max.',
'Equinox',
'Orbit.Uncertainity'
)
ds_step_1 <- dataset %>% dplyr::select(-excluded_columns) %>% na.omit## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(excluded_columns)` instead of `excluded_columns` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.rm(dataset)
str(ds_step_1)## 'data.frame': 4687 obs. of 25 variables:
## $ Absolute.Magnitude : num 21.6 21.3 20.3 27.4 21.6 19.6 19.6 19.2 17.8 21.5 ...
## $ Est.Dia.in.Miles.min. : num 0.07905 0.09076 0.14385 0.00547 0.07905 ...
## $ Est.Dia.in.Miles.max. : num 0.1768 0.203 0.3217 0.0122 0.1768 ...
## $ Relative.Velocity.km.per.sec: num 6.12 18.11 7.59 11.17 9.84 ...
## $ Relative.Velocity.km.per.hr : num 22017 65210 27327 40226 35427 ...
## $ Miles.per.hour : num 13681 40519 16980 24995 22013 ...
## $ Miss.Dist..Astronomical. : num 0.419 0.383 0.051 0.285 0.408 ...
## $ Miss.Dist..lunar. : num 163.2 149 19.8 111 158.6 ...
## $ Miss.Dist..kilometers. : num 62753692 57298148 7622912 42683616 61010824 ...
## $ Miss.Dist..miles. : num 38993336 35603420 4736658 26522368 37910368 ...
## $ Minimum.Orbit.Intersection : num 0.02528 0.18693 0.04306 0.00551 0.0348 ...
## $ Jupiter.Tisserand.Invariant : num 4.63 5.46 4.56 5.09 5.15 ...
## $ Epoch.Osculation : num 2458000 2458000 2458000 2458000 2458000 ...
## $ Eccentricity : num 0.426 0.352 0.348 0.217 0.21 ...
## $ Semi.Major.Axis : num 1.41 1.11 1.46 1.26 1.23 ...
## $ Inclination : num 6.03 28.41 4.24 7.91 16.79 ...
## $ Asc.Node.Longitude : num 314.4 136.7 259.5 57.2 84.6 ...
## $ Orbital.Period : num 610 426 644 514 496 ...
## $ Perihelion.Distance : num 0.808 0.718 0.951 0.984 0.968 ...
## $ Perihelion.Arg : num 57.3 313.1 248.4 18.7 158.3 ...
## $ Aphelion.Dist : num 2.01 1.5 1.97 1.53 1.48 ...
## $ Perihelion.Time : num 2458162 2457795 2458120 2457902 2457814 ...
## $ Mean.Anomaly : num 264.8 173.7 292.9 68.7 135.1 ...
## $ Mean.Motion : num 0.591 0.845 0.559 0.7 0.726 ...
## $ Hazardous : chr "True" "False" "True" "False" ...2. Eliminamos las columnas que están altamente co-relacionadas
Excluimos las columnas que tienen un correlación mayor al 80%. Del grupo altamenten correlacionesdos nos quedamos con una sola variable ya que todas con muy simialres.
high_correlated_columns <- find_high_correlated_columns(
feat(ds_step_1),
cutoff=0.8
)
print(high_correlated_columns)## [1] "Aphelion.Dist" "Miss.Dist..kilometers."
## [3] "Miss.Dist..Astronomical." "Miss.Dist..miles."
## [5] "Jupiter.Tisserand.Invariant" "Semi.Major.Axis"
## [7] "Relative.Velocity.km.per.sec" "Relative.Velocity.km.per.hr"
## [9] "Orbital.Period" "Est.Dia.in.Miles.max."
## [11] "Epoch.Osculation"ds_step_2 <- ds_step_1 %>% dplyr::select(-high_correlated_columns[-1])
rm(ds_step_1)
str(ds_step_2)## 'data.frame': 4687 obs. of 15 variables:
## $ Absolute.Magnitude : num 21.6 21.3 20.3 27.4 21.6 19.6 19.6 19.2 17.8 21.5 ...
## $ Est.Dia.in.Miles.min. : num 0.07905 0.09076 0.14385 0.00547 0.07905 ...
## $ Miles.per.hour : num 13681 40519 16980 24995 22013 ...
## $ Miss.Dist..lunar. : num 163.2 149 19.8 111 158.6 ...
## $ Minimum.Orbit.Intersection: num 0.02528 0.18693 0.04306 0.00551 0.0348 ...
## $ Eccentricity : num 0.426 0.352 0.348 0.217 0.21 ...
## $ Inclination : num 6.03 28.41 4.24 7.91 16.79 ...
## $ Asc.Node.Longitude : num 314.4 136.7 259.5 57.2 84.6 ...
## $ Perihelion.Distance : num 0.808 0.718 0.951 0.984 0.968 ...
## $ Perihelion.Arg : num 57.3 313.1 248.4 18.7 158.3 ...
## $ Aphelion.Dist : num 2.01 1.5 1.97 1.53 1.48 ...
## $ Perihelion.Time : num 2458162 2457795 2458120 2457902 2457814 ...
## $ Mean.Anomaly : num 264.8 173.7 292.9 68.7 135.1 ...
## $ Mean.Motion : num 0.591 0.845 0.559 0.7 0.726 ...
## $ Hazardous : chr "True" "False" "True" "False" ...3. Tomamos las columnas que mejor separan las clases.
Para realizar este paso vamos a utilizar la función feature_importance del algoritmo Random Forest. Esta función nos permite comparar las variables descuerdo a cuan buenas son para separa las clases. Luego tomaremos las 5 variables que mejor separa las clases.
result <- features_importance(ds_step_2, target = 'Hazardous')## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(target)` instead of `target` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.result##
## Call:
## randomForest(x = features, y = target, importance = TRUE)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 3
##
## OOB estimate of error rate: 0.34%
## Confusion matrix:
## False True class.error
## False 3925 7 0.001780264
## True 9 746 0.011920530plot_features_importance(result)
n_best_features = 5
# n_best_features = 15
best_features <- top_acc_features(result, top=n_best_features)## Selecting by MeanDecreaseGinibest_features## [1] "Minimum.Orbit.Intersection" "Absolute.Magnitude"
## [3] "Est.Dia.in.Miles.min." "Perihelion.Distance"
## [5] "Inclination"length(best_features)## [1] 5ds_step_3 <- ds_step_2 %>% dplyr::select(c(best_features, c(Hazardous)))## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(best_features)` instead of `best_features` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.rm(ds_step_2)str(ds_step_3)## 'data.frame': 4687 obs. of 6 variables:
## $ Minimum.Orbit.Intersection: num 0.02528 0.18693 0.04306 0.00551 0.0348 ...
## $ Absolute.Magnitude : num 21.6 21.3 20.3 27.4 21.6 19.6 19.6 19.2 17.8 21.5 ...
## $ Est.Dia.in.Miles.min. : num 0.07905 0.09076 0.14385 0.00547 0.07905 ...
## $ Perihelion.Distance : num 0.808 0.718 0.951 0.984 0.968 ...
## $ Inclination : num 6.03 28.41 4.24 7.91 16.79 ...
## $ Hazardous : chr "True" "False" "True" "False" ...4. Transformamos la varaible a predecir a tipo numericas
ds_step_4 <- ds_step_3 %>%
mutate(Hazardous = case_when(Hazardous %in% c('True') ~ 1, TRUE ~ 0))
str(ds_step_4)## 'data.frame': 4687 obs. of 6 variables:
## $ Minimum.Orbit.Intersection: num 0.02528 0.18693 0.04306 0.00551 0.0348 ...
## $ Absolute.Magnitude : num 21.6 21.3 20.3 27.4 21.6 19.6 19.6 19.2 17.8 21.5 ...
## $ Est.Dia.in.Miles.min. : num 0.07905 0.09076 0.14385 0.00547 0.07905 ...
## $ Perihelion.Distance : num 0.808 0.718 0.951 0.984 0.968 ...
## $ Inclination : num 6.03 28.41 4.24 7.91 16.79 ...
## $ Hazardous : num 1 0 1 0 1 0 0 0 0 1 ...5. Análisis Exploratorio
5.1. Boxplot comparativos
coparative_boxplot(feat(ds_step_4), to_col=n_best_features)
5.2. Histogramas y densidad
comparative_histplot(feat(ds_step_4), to_col=n_best_features)
5.3. Analizamos gráfico de normalidad univariado
## [[1]]
## [1] 3.7e-24
##
## [[2]]
## [1] 3.7e-24
##
## [[3]]
## [1] 3.7e-24
##
## [[4]]
## [1] 3.7e-24
##
## [[5]]
## [1] 3.7e-24Observaciones: Al parecer solo Perihelion.Distance parece ser normal o por lo enos la mas nromal de todas.
5.3. Test de normalidad uni-variado
uni_shapiro_test(feat(ds_step_4))## [1] "=> Variable: Minimum.Orbit.Intersection"
## [1] ""
##
## Shapiro-Wilk normality test
##
## data: features[, col_index]
## W = 0.82145, p-value < 2.2e-16
##
## [1] "=> Variable: Absolute.Magnitude"
## [1] ""
##
## Shapiro-Wilk normality test
##
## data: features[, col_index]
## W = 0.9888, p-value < 2.2e-16
##
## [1] "=> Variable: Est.Dia.in.Miles.min."
## [1] ""
##
## Shapiro-Wilk normality test
##
## data: features[, col_index]
## W = 0.41945, p-value < 2.2e-16
##
## [1] "=> Variable: Perihelion.Distance"
## [1] ""
##
## Shapiro-Wilk normality test
##
## data: features[, col_index]
## W = 0.98627, p-value < 2.2e-16
##
## [1] "=> Variable: Inclination"
## [1] ""
##
## Shapiro-Wilk normality test
##
## data: features[, col_index]
## W = 0.88555, p-value < 2.2e-16Observaciones: En todos los casos el p-valor < 0.05 y se rechaza normalidad en todas las variables. Esto coincido con los qqplot donde en todos los casos no son normales salvo Perihelion.Distance que parece tender anormalidad.
5.4. Test de normalidad muti-variado
mult_shapiro_test(feat(ds_step_4))##
## Shapiro-Wilk normality test
##
## data: Z
## W = 0.29729, p-value < 2.2e-16Observaciones: El p-valore < 0.05 por lo tanto se rechaza normalidad multi-variada. Se corresponde con el resultado de los tests de shapiro uni-variados y los qqplot’s.
5.5. Test de homocedasticidad uni-variado
uni_levene_test(feat(ds_step_4), ds_step_4$Hazardous)## [1] "=> Variable: Minimum.Orbit.Intersection"
## [1] ""
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 576.34 < 2.2e-16 ***
## 4685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] ""
## [1] ""
## [1] "=> Variable: Absolute.Magnitude"
## [1] ""
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 500.87 < 2.2e-16 ***
## 4685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] ""
## [1] ""
## [1] "=> Variable: Est.Dia.in.Miles.min."
## [1] ""
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.3586 0.5493
## 4685
## [1] ""
## [1] ""
## [1] "=> Variable: Perihelion.Distance"
## [1] ""
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 16.241 5.667e-05 ***
## 4685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] ""
## [1] ""
## [1] "=> Variable: Inclination"
## [1] ""
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 3.099 0.0784 .
## 4685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] ""
## [1] ""Observaciones
Si testeamos homocedatisidad entre cada variable y la varuabkle target encontramos que:
- Minimum.Orbit.Intersection: p-valor < 0.05 -> Rechaza homocedasticidad.
- Absolute.Magnitude: p-valor < 0.05 -> Rechaza homocedasticidad.
- Est.Dia.in.Miles.min.: p-valor > 0.05 -> No Rechaza homocedasticidad.
- Perihelion.Distance: p-valor < 0.05 -> Rechaza homocedasticidad.
- Inclination: p-valor > 0.05 -> No Rechaza homocedasticidad.
Por lo tanto solo Est.Dia.in.Miles.min. parece tener varianza simialr con la variable target(Hazardous).
5.6. Test de homocedasticidad multi-variado
multi_boxm_test(feat(ds_step_4), ds_step_4$Hazardous)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: features
## Chi-Sq (approx.) = 3154.8, df = 15, p-value < 2.2e-16Observaciones: El p-valor < 0.05 por lo tanto se rechaza la hipótesis nula y podemos decir que las variablesno son homocedasticas.
5.7. Correlaciones entre variables
plot_correlations(feat(ds_step_4))5.8. Análisis completo
5.9. PCA: Comparación de variables originales con/sin la variable a predecir.
## **Results for the Principal Component Analysis (PCA)**
## The analysis was performed on 4687 individuals, described by 6 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. for the variables"
## 4 "$var$cor" "correlations variables - dimensions"
## 5 "$var$cos2" "cos2 for the variables"
## 6 "$var$contrib" "contributions of the variables"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "summary statistics"
## 12 "$call$centre" "mean of the variables"
## 13 "$call$ecart.type" "standard error of the variables"
## 14 "$call$row.w" "weights for the individuals"
## 15 "$call$col.w" "weights for the variables"
## **Results for the Principal Component Analysis (PCA)**
## The analysis was performed on 4687 individuals, described by 5 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. for the variables"
## 4 "$var$cor" "correlations variables - dimensions"
## 5 "$var$cos2" "cos2 for the variables"
## 6 "$var$contrib" "contributions of the variables"
## 7 "$ind" "results for the individuals"
## 8 "$ind$coord" "coord. for the individuals"
## 9 "$ind$cos2" "cos2 for the individuals"
## 10 "$ind$contrib" "contributions of the individuals"
## 11 "$call" "summary statistics"
## 12 "$call$centre" "mean of the variables"
## 13 "$call$ecart.type" "standard error of the variables"
## 14 "$call$row.w" "weights for the individuals"
## 15 "$call$col.w" "weights for the variables"5.10. PCA: Con observaciones discriminadas pro clase. Primero quitamos
outliers para poder ver con mas claridad el biplot.
ds_without_outliers <- filter_outliers(ds_step_4, max_score=0.52)
plot_robust_pca(ds_without_outliers)
6. Train test split
c(raw_train_set, raw_test_set) %<-% train_test_split(ds_step_4, train_size=.8)## [1] "Train Set size: 3749"
## [1] "Test set size: 938"7. Método SMOTE para balancear el dataset.
# balanced_train_set <- smote_balance(raw_train_set, raw_train_set$Hazardous)
#rm(raw_train_set)
balanced_train_set <- raw_train_set8. Escalamos las variables numéricas(Restamos la media y dividimos por el
desvío).
train_set <- balanced_train_set %>%
mutate_at(vars(-Hazardous), ~(scale(.) %>% as.vector))
test_set <- raw_test_set %>%
mutate_at(vars(-Hazardous), ~(scale(.) %>% as.vector))
rm(balanced_train_set)
rm(raw_test_set)9. Clasificacion
9.1. Entrenamos un modelo LDA
reg_formula <- formula(Hazardous~.)
lda_model <- lda(reg_formula, train_set)
lda_test_pred <- predict(lda_model, test_set)
plot_cm(lda_test_pred$class, target(test_set))
graphics.off()
plot_roc(lda_test_pred$class, target(test_set))##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 157
## Mean of positive(s): 1.662
## Variance of positive(s): 0.2251
## ===== Negative(s) =====
## Number of negative(s): 781
## Mean of negative(s): 1.049
## Variance of negative(s): 0.04635
## ===== AUC =====
## Area under curve: 0.8069
##
## =========================
## FPR TPR
## 0.00000 0.0000
## 0.04866 0.6624
## 1.00000 1.0000f_beta_score(lda_test_pred$class, target(test_set), beta=2)## [1] "F2Score: 0.286944075672088"lda_model$scaling## LD1
## Minimum.Orbit.Intersection -1.25346108
## Absolute.Magnitude -1.58370375
## Est.Dia.in.Miles.min. -0.41233546
## Perihelion.Distance 0.06937507
## Inclination -0.018138089.2. Entrenamos un modelo QDA
qda_model <- qda(reg_formula, train_set)
qda_test_pred <- predict(qda_model, test_set)
plot_cm(qda_test_pred$class, target(test_set))
plot_roc(qda_test_pred$class, target(test_set))
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 157
## Mean of positive(s): 1.93
## Variance of positive(s): 0.06557
## ===== Negative(s) =====
## Number of negative(s): 781
## Mean of negative(s): 1.003
## Variance of negative(s): 0.002558
## ===== AUC =====
## Area under curve: 0.9637
##
## =========================
## FPR TPR
## 0.000000 0.0000
## 0.002561 0.9299
## 1.000000 1.0000f_beta_score(qda_test_pred$class, target(test_set), beta=2)## [1] "F2Score: 0.0676674327386125"9.3. Entrenamos un modelo RDA
rda_model <- rda(reg_formula, train_set)
rda_test_pred <- predict(rda_model, test_set)
plot_cm(rda_test_pred$class, target(test_set))
plot_roc(rda_test_pred$class, target(test_set))
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 157
## Mean of positive(s): 1.93
## Variance of positive(s): 0.06557
## ===== Negative(s) =====
## Number of negative(s): 781
## Mean of negative(s): 1.003
## Variance of negative(s): 0.002558
## ===== AUC =====
## Area under curve: 0.9637
##
## =========================
## FPR TPR
## 0.000000 0.0000
## 0.002561 0.9299
## 1.000000 1.0000f_beta_score(rda_test_pred$class, target(test_set), beta=2)## [1] "F2Score: 0.0676674327386125"9.4. Entrenamos un modelo de regresión logística
rl_model <- glm(reg_formula, train_set, family=binomial)
rl_test_pred <- predict(rl_model, test_set)
rl_threshold <- 0.4
rl_test_pred_threshold <- ifelse(rl_test_pred >= rl_threshold, 1, 0)
plot_cm(rl_test_pred_threshold, target(test_set))
plot_roc(rl_test_pred_threshold, target(test_set))
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 157
## Mean of positive(s): 0.7389
## Variance of positive(s): 0.1942
## ===== Negative(s) =====
## Number of negative(s): 781
## Mean of negative(s): 0.02177
## Variance of negative(s): 0.02132
## ===== AUC =====
## Area under curve: 0.8585
##
## =========================
## FPR TPR
## 0.00000 0.0000
## 0.02177 0.7389
## 1.00000 1.0000f_beta_score(rl_test_pred_threshold, target(test_set), beta=2)## [1] "F2Score: 0.76215505913272"9.5. Entrenamos un modelo SVM
svm_model <- svm(reg_formula, train_set, kernel="radial")
svm_test_pred <- predict(svm_model, test_set)
svm_threshold <- 0.5
svm_test_pred_threshold <- ifelse(svm_test_pred >= rl_threshold, 1, 0)
plot_text_cm(svm_test_pred_threshold, target(test_set))## Prediction
## Reality 0 1
## 0 760 23
## 1 21 134plot_roc(svm_test_pred_threshold, target(test_set))
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 157
## Mean of positive(s): 0.8535
## Variance of positive(s): 0.1258
## ===== Negative(s) =====
## Number of negative(s): 781
## Mean of negative(s): 0.02689
## Variance of negative(s): 0.0262
## ===== AUC =====
## Area under curve: 0.9133
##
## =========================
## FPR TPR
## 0.00000 0.0000
## 0.02689 0.8535
## 1.00000 1.0000f_beta_score(svm_test_pred_threshold, target(test_set), beta=2)## [1] "F2Score: 0.855683269476373"9.6. Entrenamos un modelo XGBoost
xgboost_model <- xgboost(
as.matrix(feat(train_set)),
target(train_set),
eta = 0.1,
max_depth = 10,
nround=5000,
objective = "binary:logistic",
nthread = 24
)## [01:02:06] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
## [1] train-logloss:0.599057
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## [5000] train-logloss:0.000629xgb_test_pred <- predict(xgboost_model, as.matrix(feat(test_set)))
xgb_threshold <- 0.5
xgb_test_pred_threshold <- ifelse(xgb_test_pred >= xgb_threshold, 1, 0)
plot_cm(xgb_test_pred_threshold, target(test_set))
plot_roc(xgb_test_pred_threshold, target(test_set))
##
## Method used: empirical
## ===== Positive(s) =====
## Number of positive(s): 157
## Mean of positive(s): 0.9682
## Variance of positive(s): 0.03103
## ===== Negative(s) =====
## Number of negative(s): 781
## Mean of negative(s): 0
## Variance of negative(s): 0
## ===== AUC =====
## Area under curve: 0.9841
##
## =========================
## FPR TPR
## 0 0.0000
## 0 0.9682
## 1 1.0000f_beta_score(xgb_test_pred_threshold, target(test_set), beta=2)## [1] "F2Score: 0.974358974358974"10. KMeans Clustering
10.1. Primero definimos cuantos grupos utilizar.
Típica con estimadores de la normal.
scaled_data_1 <- feat(ds_step_4) %>% mutate(~(scale(.) %>% as.vector))Escalamiento diferente de la tipica normal.
scaled_data_2 <- apply(
feat(ds_step_4),
2,
function(x) { (x - min(x)) / (max(x) - min(x))}
)clustering_metrics_plot(scaled_data_1)
clustering_metrics_plot(scaled_data_2)
10.2. Kmeans
n_clusters <- 2
kmeans_model <- kmeans(scaled_data_1, n_clusters)
km_ds_step_4 <- data.frame(ds_step_4)
km_ds_step_4$kmeans <- kmeans_model$cluster10.2. biplot
ds_without_outliers <- filter_outliers(km_ds_step_4, max_score=0.5)
plot_robust_pca(
ds_without_outliers %>% dplyr::select(-kmeans),
groups = factor(ds_without_outliers$kmeans),
)
10.3 Hierarchical Clustering
Matriz de distancias euclídeas
mat_dist <- dist(x = ds_step_4, method = "euclidean") Dendrogramas (según el tipo de segmentación jerárquica aplicada)
hc_complete <- hclust(d = mat_dist, method = "complete")
hc_average <- hclust(d = mat_dist, method = "average")
hc_single <- hclust(d = mat_dist, method = "single")
hc_ward <- hclust(d = mat_dist, method = "ward.D2")Calculo del coeficiente de correlación cofenetico
cor(x = mat_dist, cophenetic(hc_complete))## [1] 0.8024213cor(x = mat_dist, cophenetic(hc_average))## [1] 0.8064622cor(x = mat_dist, cophenetic(hc_single))## [1] 0.5078482cor(x = mat_dist, cophenetic(hc_ward))## [1] 0.5889577Construcción de un dendograma usando los resultados de la técnica de Ward
n_clusters <- 2
graphics.off()
plot(hc_ward )
rect.hclust(hc_ward, k=n_clusters, border="red")hc_ds <- data.frame(ds_step_4)
hc_ds$her_ward <- cutree(hc_ward, k=n_clusters)ds_without_outliers <- filter_outliers(hc_ds, max_score=0.57)
plot_robust_pca(
ds_without_outliers %>% dplyr::select(-her_ward),
groups = factor(ds_without_outliers$her_ward),
colours=c("orange","cyan","blue","magenta","yellow","black"),
labels=c("grupo 1", "grupo 2","grupo 3","grupo 4","grupo 5","grupo 6")
)
plot_robust_pca(ds_without_outliers)
Realizado por Adrian Marino
adrianmarino@gmail.com